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cognee: Skill for Claude Code

.agents/skills/cognee-integrations/SKILL.md

cognee-integrations is a skill for Claude Code, Codex from topoteretes/cognee. It costs 72 tokens per session (1,099 once invoked), scanned A, original, Apache-2.0.

Instructions for connecting Cognee to outside services such as language-model providers, embedding providers, databases, object storage, and an MCP server. MCP is a way for development tools to call an application's functions.

In plain words
What is it for?
Use it to configure providers such as OpenAI, Gemini, Anthropic, or Ollama; databases such as Postgres or Neo4j; S3 storage; or IDE integration.
Why use it?
It reduces configuration errors when replacing Cognee's default services or connecting it to an IDE or remote data store.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions Claude Code; installed under .agents/ (shared by several agents).

This is topoteretes/cognee's own configuration. It tells Claude Code and Codex how to work on cognee itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything cognee configures →

not rated 33krepo +157 changed 7d ago A scan SkillSpector: pass 72 tokens original Apache-2.0
View source ↗ topoteretes/cognee cognee.ai
About the project

Cognee is an AI memory platform that stores information in a self-hosted knowledge graph so agents can retain context across sessions. It ingests data, connects related information, and helps agents retrieve it for reasoning and actions. The catalogue includes skills and instructions that extend agent workflows around Cognee.

topoteretes/cognee · 33,094 stars · on GitHub · cognee.ai

Reuse

Borrowing it

Nothing to install: this file belongs to topoteretes/cognee. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/topoteretes/cognee/main/.agents/skills/cognee-integrations/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/topoteretes/cognee

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for cognee-integrations

README.md
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Your own site
<a href="https://agentmods.dev/skills/topoteretes/cognee/cognee-integrations"><img src="https://agentmods.dev/badge/skills/topoteretes/cognee/cognee-integrations/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for cognee-integrations

Your own site · 80×15
<a href="https://agentmods.dev/skills/topoteretes/cognee/cognee-integrations"><img src="https://agentmods.dev/badge/skills/topoteretes/cognee/cognee-integrations.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 72 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,099 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00072 $0.01099
Opus 5.5 $0.00029 $0.00440
Sonnet 5.5 $0.00014 $0.00220
Haiku 4.5 $0.00007 $0.00110

Measured 7d ago against content hash d295595bfd5e, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

cognee-integrations scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.agents/skills/cognee-integrations/SKILL.md · 74 lines

How it starts

The opening of the file, as written. The whole thing — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Set up cognee integrations

All integration config is environment variables (.env). The authoritative, always-current list with commented examples is .env.template at the repo root — check it before inventing variable names. Install the matching extra before switching a backend (e.g. pip install cognee[postgres]).

LLM providers

Default is OpenAI (LLM_API_KEY is all you need). To switch, set LLM_PROVIDER, LLM_MODEL, LLM_API_KEY, and (where relevant) LLM_ENDPOINT / LLM_API_VERSION:

  • Azure OpenAI: LLM_PROVIDER=azure, LLM_MODEL=azure/gpt-4o-mini, endpoint + api version required.
  • Gemini (no extra needed): LLM_PROVIDER=gemini, LLM_MODEL=gemini/gemini-2.0-flash-exp.
  • Anthropic (cognee[anthropic]): LLM_PROVIDER=anthropic, model e.g. claude-3-5-sonnet-20241022.
  • Ollama, local (cognee[ollama]): LLM_PROVIDER=ollama, LLM_ENDPOINT=http://localhost:11434/v1, and set the embedding block + HUGGINGFACE_TOKENIZER too.
  • Custom / OpenRouter / vLLM: LLM_PROVIDER=custom with the provider's OpenAI-compatible endpoint.
  • AWS Bedrock (cognee[aws]): LLM_PROVIDER=bedrock + AWS credentials/region.

The classic trap: LLM and embeddings are configured independently (EMBEDDING_PROVIDER, EMBEDDING_MODEL, EMBEDDING_ENDPOINT, EMBEDDING_API_KEY). Configuring only one leaves the other on OpenAI — either keep a valid OpenAI key or configure both.

Databases

  • Relational (DB_PROVIDER): sqlite (default) or postgres (cognee[postgres]; host/port/user/password/name via DB_* vars).
  • Vector (VECTOR_DB_PROVIDER): lancedb (default), pgvector (cognee[postgres], needs VECTOR_DB_URL), neptune_analytics (cognee[neptune]), turso (cognee[turso]). Anything else (ChromaDB, Qdrant, Weaviate, Milvus, …) lives in community adapters — install from https://github.com/topoteretes/cognee-community and register with use_vector_adapter before use; setting VECTOR_DB_PROVIDER alone raises "Unsupported vector database provider".
  • Graph (GRAPH_DATABASE_PROVIDER): ladybug (default), neo4j (cognee[neo4j], bolt URL + credentials), neptune (cognee[neptune]), ladybug-remote, postgres (no raw Cypher / natural-language search).

Read the full file on GitHub · 74 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 7d ago Changed d295595bfd5e
  2. 1mo ago Changed 07d782b0384a
  3. 2mo ago First seen · 74 lines · 72 tokens per session scan A b9c18daf592c

Subscribe to this mod's changes

cognee-integrations is a skill published in the GitHub repository topoteretes/cognee (33,094 stars, last pushed today), licensed Apache-2.0. It adds 72 tokens to every session and 1,099 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Data and AIDatabasesaiai-agentsai-memorycognitive-architecturegraph-databasevector-database
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